Panel Data Research Center at Keio University
DISCUSSION PAPER SERIES
DP2015-006
December, 2015

Intensive and Extensive Margins of Japanese Male and Female Workers- Evidence from the Tax Policy Reform in JapanYusuke Inoue＊【Abstract】
Using the annual micro panel data of Keio Household Panel Survey, I estimated labor
supply models to identify the intensive and extensive margins of Japanese male andfemale workers. My empirical results contradict earlier studies which finds large
intensive margin of male workers. The findings are while, for all male workers, hours
worked elasticity and labor force participation elasticity are quite small (0.009 and0.0025 respectively), extensive margin of non-regular workers shows big sensitive
response (0.365). On the other hand, intensive margins of both workers are small
against wage changes. The investigation by separating age groups (over 55 and under54) shows that the sensitive labor participation response is caused by elderly
non-regular male workers. Furthermore, the comparison between male and female
labor supply responses ensure that both margins of male are much smaller than thoseof females. The result also reveal interesting feature of female work behavior that theintensive margin of female regular workers is as small as that of male.
Keywords: Taxes, Japanese Male Labor Supply, Lifecycle-Consistent Fixed Effects
model, Selection, and Endogeneity＊

Deputy Director in the Japanese Ministry of Health, Labour and Welfare

Panel Data Research Center at Keio University
Keio University

Intensive and Extensive Margins of Japanese Male and Female Workers- Evidence from the Tax Policy Reform in JapanYusuke Inoue1

Abstract
Using the annual micro panel data of Keio Household Panel Survey, I estimated labor supply
models to identify the intensive and extensive margins of Japanese male and female workers.
My empirical results contradict earlier studies which finds large intensive margin of maleworkers. The findings are while, for all male workers, hours worked elasticity and labor force
participation elasticity are quite small (0.009 and 0.0025 respectively), extensive margin of nonregular workers shows big sensitive response (0.365). On the other hand, intensive margins ofboth workers are small against wage changes. The investigation by separating age groups (over55 and under 54) shows that the sensitive labor participation response is caused by elderly nonregular male workers. Furthermore, the comparison between male and female labor supply
responses ensure that both margins of male are much smaller than those of females. The result
also reveal interesting feature of female work behavior that the intensive margin of female
regular workers is as small as that of male.
Keywords: Taxes, Japanese Male Labor Supply, Lifecycle-Consistent Fixed Effects model,
Selection, and Endogeneity

1

Deputy Director in the Japanese Ministry of Health, Labour and Welfare.
E-mail: inoue-yuusuke@mhlw.go.jp

11. Introduction
Estimating the effect of income taxes on the labor supply has been a centerpiece in labor
economics researches over decades since labor supply elasticity estimation is essential for the
calculation of social welfare loss caused by public policy. However, compared to works in U.S.
and European countries, relatively small portions of researches have addressed to identify labor
supply elasticity in Japan. Especially, there are few results regarding the labor supply responses
of males who play a core role in the Japanese labor market.
The main purpose of this paper is providing new evidence in the Japanese male labor supply
responses based on the lifecycle consistent structural model with recently developed micro-panel
data, exploiting tax policy changes in 2006 and 2007 to deal with potential endogeneity problem.
Also, to check and compare the validity of empirical specification used in this paper, I similarly
estimate female labor supply responses since those studies have been more accumulated thanmales. Besides, to respond growing concerns on the Japanese labor market in which non-regular
workers have been increasing, I estimate two separate labor supply responses for non-regular andregular workers for males.
Although bunch of literatures try to capture labor supply elasticity, there is no consensus on
how exactly male and female response against tax and wage changes. Keane (2011) surveys
existed labor supply literatures and finds that uncompensated wage elasticity for males areranged between -0.47 and 0.7, and its average is 0.04, while the elasticity for female is between 0.2 and 0.89, and its average is 0.292. Bargain, et al. (2012) take a large-scale international
comparison of labor supply elasticities. Their survey shows that the wage elasticities acrosscountries are relatively small for both sexes, but the labor force participation elasticities are2

Female literatures surveyed by Keane (2011) are only 10, meanwhile 28 male literatures are surveyed.

2bigger than the wage elasticities. Also, there is a view that male workers show relatively smalllabor supply elasticities compared to female workers.
On the other hand, although there are empirical analyses for elderly and female labor supply
in Japan, as far as I know, there are few articles investigating male labor supply issue. Asano(1997) estimates the price elasticity of goods by Almost Ideal Demand System (AIDS) withpooled-aggregated data of 47 prefectures. In this research, he finds compensated wage elasticity
is 0.39. Yamada, et al. (1999) employ AIDS method to estimate labor supply of men aged 25-39
with aggregated data on prefecture basis. His estimation also indicates relatively big wage
elasticity (0.37). Kuroda and Yamamoto (2008 (a)) establish aggregated data by prefecture, age
and sex to estimate Frish elasticity with life-cycle consistent labor supply model. Their estimatesof intensive margin for male workers are in a range of 0.14 to 0.24.3
However, although Japanese economists find relatively large value of intensive marginamong male workers compared to findings in other countries, more accurate estimation should
be required utilizing micro data. In my best knowledge, only work so far estimating male labor
supply responses with consideration of personal income tax using micro data is Bessho andHayashi (2005), and their sequential work of Bessho and Hayashi (2011). Their 2011 paper tries
three ML methods with the largest cross-sectional labor survey of Japan (Shugyo Kozo Kihon
Chosa) focusing on the prime-age male aged 25 to 55. While their estimates show large
variations of values of intensive margin, they conclude that their findings are still bigger4 thanthose estimated in other countries’ studies.

3

Their research (2008 (b)) is one of the most related works of this paper. They try to estimate the wage elasticitiesof two different employment types of female workers by the similar method used here. However, the paper does notconsider the presence of income tax.
4
Three models used in the paper show the average of uncompensated elasticity for single-earner-male withdependents is 0.288.

3

As we have seen above, there are scant amount of literatures regarding estimates of Japanesemale labor supply, and only Bessho and Hayashi estimates the labor supply response considering
the tax system with cross-sectional micro data. However, surprisingly, there are no attempts toestimate male labor supply in Japan with micro-panel data. The simple reason may be that there
had not been available micro panel data which contains necessary information of males, althoughrelatively long panel data has been existed for females in Japan. Thus, this paper exploits
recently developed Keio Household Panel Survey (KHPS), which is the first comprehensive
survey to respond such a demand, and has been implemented annually since 2004.
The empirical strategy used here tackles the simultaneity problem of working hours and aftertax wages utilizing Japanese personal income tax reforms implemented in 2006 and 2007. Since
the tax reform which is not implemented considering change of each individual’s working hours
could generate exogenous variation in after-tax wage, the reforms are typically thought toprovide ideal natural experiments. The empirical method also considers other well-known
problems (Selection bias and division bias) which hamper researchers to get the consistentestimators.
The contributions of this paper are the first to estimate lifecycle-consistent labor supply
elasticities of Japanese males and females with micro-panel data, taking into account thepresence of nonlinear taxation with a two-stage budgeting framework following the method ofKimmel and Kniesner (1998), and Ziliak and Kniesner (1999). Secondary, the paper
distinguishes different labor responses of non-regular and regular workers, considering
multinomial labor participation decisions with the correction methods of Dubin and McFadden(1980), and Semykina and Wooldridge (2010).

4My empirical results contradict earlier studies which finds large intensive margin of maleworkers. The findings are while, for all male workers, hours worked elasticity and labor force
participation elasticity are quite small (0.009 and 0.0025 respectively), extensive margin of nonregular workers shows big sensitive response (0.365). On the other hand, intensive margins ofboth workers are small against wage changes. The investigation by separating age groups (over55 and under 54) shows that the sensitive labor participation response is caused by elderly nonregular male workers. Furthermore, the comparison between male and female labor supply
responses ensure that both margins of male are much smaller than those of females. The result
also reveal interesting feature of female work behavior that the intensive margin of female
regular workers is as small as that of male.
The paper is organized as follows. The next section presents brief explanation of Japaneselabor market and tax policy. The section 3 describes theoretical background of labor supply with
non-linear tax system and empirical strategies. The section 4 explains about the dataset and key
variables’ data construction. In the section 5, the results are discussed. Finally, I will makeconclusions from the obtained results.

2.1 Japanese labor market
Traditionally, core workers of Japanese labor market consist of male regular workers who are
typically considered as permanent contract workers until mandatory retirement. However,proportions of non-regular workers, who consist of fixed-term contract workers, dispatchedworkers and part-time workers, have been growing in all cohorts. As table1 shows, proportionsof non-regular workers are rapidly increasing from 1995 especially in younger cohorts. Initialsurge for all cohorts had been caused by the long depression after housing bubble exploded. At

5that time, firms suffering from increasing debt had started to adjust employment and decrease
wage level. This effect has disproportional effect among workers. Genda (2001) highlights thatthe cost of the long recession in Japanese economy was born disproportionately by young people
because cutting new hiring is much easier than firing incumbent workers. The white paper oflabor economy by Japanese Ministry of Health, Labor and Welfare (2012) also points out thatreasons why this trend still continues are : 1) firms are willing to save labor cost and respondbusiness cycle more flexibly, 2) workers tend to choose comfortable working time for their life
style.
It is worthwhile mentioning types of non-regular workers to introduce their working patterns.
In this paper, non-regular workers consists of part-timers, fixed-term contract workers(Keiyaku
Shain), fixed-term contract after retiring (Syokutaku), and dispatched workers. Part-timers
account for the largest portion of Japanese non-regular workers. They are defined as those whowork shorter than other regular workers in workplaces. The Keiyaku Shain is the person whose
work contract is not permanent and limited for several years, while they work full time.
Syokutaku is a subset of Keiyaku Shain. But, they are employees who have reached mandatory
retirement age and are rehired under temporary working status. Dispatched workers are persons
who are dispatched from their employer to user-companies. Of all those workers, Syokutaku
would show quite different working patterns because they basically got the retirement benefits,so, unlike younger non-regular workers, they are considered to work in a more flexibly way.
Thus, when we estimate labor supply, we need to be careful about their existence.
This increasing number of non-regular workers has caused political concerns because theirsalaries level is lower and employment protection is weaker than regular workers, which could

6cause fears of a future expansion of poverty and an increasing burden of social security. In an
economics sense, it is also important to identify their behavioral features against wage changes.

Table1

Trend of Non-Regular Workers' Ratio by ages (%)
Male

Year

Female

15-24

25-34

35-44

45-54

54-64

15-24

25-34

35-44

45-54

54-64

1990

19.9

3.2

3.3

4.3

22.7

20.7

28.2

49.7

44.8

45.0

1995

23.7

2.9

2.4

2.9

17.8

28.3

26.8

49.0

46.9

43.9

2000

38.6

5.7

3.8

4.2

17.7

42.3

32.0

53.3

52.0

55.9

2005

44.6

13.2

7.1

9.1

27.8

51.3

38.3

54.4

56.7

61.4

2010
41.2
13.3
8.2
7.9
27.4
50.0
41.6
51.1
58.0
Source: Labor Force Survey ( Japanese Ministry of Internal Affairs and Communications)

63.9

2.2 Tax policy reform in Japan
In this section, I will explain about Japanese tax policy, especially for personal income taxes
and its recent reforms which are considered in the analysis. Earning of employed workers inJapan is withheld by personal income tax system which is classified as two components; national
income tax withheld by national government and inhabitant tax withheld by local government.
Combined income tax brackets are progressive, ranging from 10% to 50% corresponding totaxable income levels. The recent tax reform in which tax brackets were changed is the 2007 tax
reform. One of objectives of this reform is transforming the fiscal resource from the national
government to local governments without putting additional income tax burden to people. Thus,
even though tax brackets for each tax system have been reformed, its combined average tax was
unchanged before this reform.
Another thing we have to consider is existence of tax deductions. Several deductions areapplied for primary earners; basic allowance, allowance for spouse and dependents, and7

employment income deductions. When taxable income is calculated, gross annual earning is
subtracted by these deductions. Basic allowance is deductible regardless of their income level.
Spouse allowance is applicable, if gross annual income of spouses of primary earners inhouseholds does not exceed 1,003,000 yen. Similarly, the allowance of dependents, who are aged
between 16 and 23, and members of the same household of primary earners and do not work, isconsidered in the paper. Furthermore, employment income deduction is applied depending on
their income level.
Furthermore, I should note the most important feature of recent Japanese tax reforms which
are used as instruments for the wage equation to overcome potential endogeneity problem.
“Permanent tax cut” policy was implemented from 1999 which aimed for reducing national
income tax rate by 20% (upper limit 250,000 yen) and inhabitant tax rate by 15% (upper limit
40,000 yen) respectively. However, building upon the gradual economic recovery in 2000s, thetax reduction scheme was abandoned partially in 2006 and completely in 2007. As Blundell et al.
(1998) suggest, tax reforms provide researchers to estimate labor supply effects by changing
after-tax wage rate of workers. Utilizing these sequence tax reforms, I instrument wage rate withother variables to deal with possible endogeneity problem. Table 2 shows the summary as
mentioned above.

8

Table 2

Personal Income Tax System in Japan (1,000 yen)
Before 2006

Tax Brackets

Deductions
Employment Income Deductions

National Income Tax
Inhabintant Tax
10 % (0 - 3,300)
3 % (0 - 2,000)
20 % (- 9,000)
8 % (- 7,000)
30 % (-18,000)
10 % (over)
37% (over)650 (0-1,625)
40 % (-1,800)
30%+180(-3,600)
20%+540(-6,600)
10%+1,200(-10,000)
5%+1,700(over)
380
380
380

Basic Allowance
Allowance for a spouse
Allowance for dependents
Permanent Tux Cut Policy(20% cut for national income tax) 10%(15% cut for inhabitant income tax) (upper limit 125)
Source: Japanese Ministry of Finance

650 (0-1,625)
40 % (-1,800)
30%+180(-3,600)
20%+540(-6,600)
10%+1,200(-10,000)
5%+1,700(over)
330
330
330
7.5%
(upper limit 20)

After 2007 Reform
National Income Tax
5 % (0-1,950)
10 % ( -3,300)
20% (- 6,950)
23 %(-9,000)
33 % (-18,000)
40% (over)Inhabintant Tax

Same

Same

Abolished

Abolished

all brackets
10 %3.1. λ-consistent lifecycle labor supply model
Here, I present the theoretical background of this paper. A big innovation in estimating life
cycle labor supply has been invented by Heckman and Macurdy (1980) who assume a latent
worker specific effect (λ: Marginal utility of wealth) is unchanged over time. Building upon theassumption that intertemporal utilities and budget constraints are separable between times,
Blomquist (1985) shows that demand functions of worked hours can be obtained in the two-stage
budgeting method exploiting time-invariant marginal utility of wealth (λ) of individuals. To
show this λ-consistent approach, I briefly introduce the discussion of Ziliak and Kniesner (1998).
Supposing that the consumer has concave preferences over consumption, 𝑐𝑡 , and working
hours, ℎ𝑡 , the objective of this consumer is maximizing intertemporally separable present
discounted lifetime utility,(1)

max ∑𝑡(1 + 𝛿)−𝑡 𝑈(𝑐𝑡, ℎ𝑡 ), which is subject to the lifetime asset constraint,

9

(2)

𝐴𝑡 = (1 + 𝑟𝑡 )𝐴𝑡−1 + 𝑤𝑡 ℎ𝑡 − 𝑐𝑡 − 𝑇(𝐼𝑡 ), where 𝐴𝑡 , 𝑟𝑡 , 𝑤𝑡 , 𝑇(∙) and 𝐼𝑡 denote the saving at the end of time t, the real interact rate, thehourly wage rate, non-linear tax function and taxable income (𝐼𝑡 = 𝑤𝑡 ℎ𝑡 + 𝑟𝑡 𝐴𝑡 − 𝐸𝑡 ) with
exemptions 𝐸𝑡 .
Supposing interior solutions and using the implicit function theorem, we can solve fordemand functions of consumption and working hours as follows.
𝑟

(3)

𝑡+1
𝑐𝑡 = 𝑐{𝜆(1 + 𝛿)𝑡 [1 − (1+𝑟
) 𝑇 ′ (𝐼𝑡+1 )], 𝑤𝑡 [1 − 𝑇 ′ (𝐼𝑡 )]}

(4)

𝑡+1
ℎ𝑡 = ℎ{𝜆(1 + 𝛿)𝑡 [1 − (1+𝑟
) 𝑇 ′ (𝐼𝑡+1 )], 𝑤𝑡 [1 − 𝑇 ′ (𝐼𝑡 )]}

𝑡+1

𝑟

𝑡+1

Here, we employ two-stage budgeting method by Blomquist (1985). In the first stage, theconsumer allocates saving to equalize the discounted expected marginal utility of wealth overtime, which means savings of 𝐴𝑡 and 𝐴𝑡−1 are optimized as 𝐴∗𝑡 and 𝐴∗𝑡−1. The asset constraint isslightly changed as,(5)𝐴∗𝑡 = (1 + 𝑟𝑡 )𝐴∗𝑡−1 + 𝑤𝑡 ℎ𝑡 − 𝑐𝑡 − 𝑇(𝐼𝑡 ), then the first-order conditions in this stage become,
(6)
(7)

𝜕𝑈
𝜕𝑐𝑡
𝜕𝑈
𝜕ℎ𝑡

=𝜆
= 𝜆𝑤𝑡 [1 − 𝑇 ′ (𝐼𝑡 )]Plug (6) and (7) into the asset constraint (5), we can see that 𝜆 should be characterized as afunction of optimized savings (𝐴∗𝑡 and 𝐴∗𝑡−1 ). Again, the implicit function theorem tells us thedemand functions are,(8)𝑐𝑡 = 𝑐{𝑤𝑡 [1 − 𝑇 ′ (𝐼𝑡 )], 𝐴∗𝑡 , 𝐴∗𝑡−1 }(9)ℎ𝑡 = ℎ{𝑤𝑡 [1 − 𝑇 ′ (𝐼𝑡 )], 𝐴∗𝑡 , 𝐴∗𝑡−1 }10The formulas allow researchers to make empirical analysis easier since saving information
behave as sufficient statics representing past and future information of individuals.

3.2 Empirical specifications
I employ a traditional labor supply model described below(10)

𝒉𝒊𝒕 = 𝜶𝒊 + 𝜷𝟏 𝐥𝐨𝐠(𝒘𝒊𝒕 ) + 𝑨𝒊𝒕 + 𝑨𝒊𝒕−𝟏 + 𝜷𝟐 𝑿𝒊𝒕 + 𝜺𝒊𝒕, where ℎ𝑖𝑡 and 𝑤𝑖𝑡 is working hours per a week and the after-tax hourly wage respectively, andthe matrix 𝑋𝑖𝑡 includes exogenous variables relevant to the decision of labor supply, such as
education, age, tenure and family backgrounds. 𝛼𝑖 catches unobserved individual heterogeneity
to represent workers’ potential ability. As bunch of literatures suggest, the key parameter 𝛽1
would be biased by various ways. First, since the progressive tax is incorporated in the process of
calculating the after-tax wage, persons working longer will face less hourly wage, which would
cause downward bias in the estimator. Also, individual unobserved characteristics, such as
ability, would affect working hour decision and wage rate. Furthermore, as Borjas (1980) points
out, when researchers use working hours, which is used as an independent variable, to constructhourly wage, it would create well known “division bias” that will cause direct negative effect.
Lastly, the pioneering work by Heckman (1979) revealed an econometric problem caused byunobserved reservation wage for people who choose not to work, which is known as a selfselection bias problem.
To overcome these endogeneity, self-selection, and division bias problems, I employ four-step
empirical strategies described in the next section.

3.3 Four step estimation
11

Unlike previous literatures which focus on two labor force states (non-working and working),
this paper tackles three working states (non-working, non-regular working and regular working).
The main reason is, as I previously mentioned, that work condition and salary of regular workers
whose work contracts are typically permanent are quite different from non-regular workers inJapan. To respond the growing concern of increasing non-regular workers, this research sheds
light on how different the behavioral patterns of those workers are.

[1st step: Estimating multinomial labor status probability]
The two-step procedures begin with estimating the participation decision equation (11) for
each year by multinomial logit model with vectors of 𝑋𝑖𝑗 , consisting of non-labor income,house-owner and co-resident with parents dummies, age, education, tenure and dummies of thepresence of children under 6 years.
(11)

exp(𝛾𝑗 𝑋𝑖𝑗 )𝑃𝑟𝑖𝑗 = ∑ 𝑒𝑥𝑝(𝛾 𝑋 )𝑗 𝑖𝑗

j={regular, non-regular, non-work}

Sequential wage and working-hours equations are affected by sample selection bias which is
caused by the truncated sample, so we have to control unobserved reservation wage of nonparticipants in labor market. Dubin and McFadden (1984) invented the method to correct thisbias to get consistent estimator in the multinomial decision setting.
To describe this approach, consider the following model:
(12)

∗
𝑦𝑖𝑗𝑡
= 𝛽𝑡 𝑋𝑖𝑗𝑡 + 𝜀𝑖𝑗𝑡

(13)

∗
𝐼𝑖𝑗𝑡
= 𝛾𝑗𝑡 𝑍𝑖𝑗𝑡 + 𝑣𝑖𝑗𝑡𝑦 ∗ = 𝑦 ∗ 𝑖𝑓 𝐼 ∗ > 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 0, where the employment status is represented by j, and i and t indicates individuals and time. 𝑦 ∗is certain outcome and I is an indicator variable. Assume the selection rule is followed by:
12

(14)

∗
∗
∗
𝐼𝑖𝑗𝑡
= 𝑗 iff 𝐼𝑖𝑗𝑡
> max 𝐼𝑖ℎ𝑡

j=1,…,M; j≠hFollowing the discussion of Vella (1998), this selection rule becomes,
(15)

∗
𝐼𝑖𝑗𝑡
= 𝑗 iff 𝛾𝑗𝑡 𝑍𝑖𝑗𝑡 − 𝛾ℎ𝑡 𝑍𝑖ℎ𝑡 > 𝑣𝑖ℎ𝑡 − 𝑣𝑖𝑗𝑡

Defining 𝑣ℎ𝑖𝑡 − 𝑣𝑗𝑖𝑡 ≡ 𝜁𝑗ℎ𝑖𝑡 and taking expectations of (12) conditional on the choice of j,(16)

E[𝑦𝑖𝑗𝑡 |𝜁𝑖1𝑡 , … , 𝜁𝑖𝑀𝑡 ] = 𝛽𝑡 𝑋𝑖𝑗𝑡 + 𝐸[𝜀𝑖𝑗𝑡 |𝜁𝑖1𝑡 , … , 𝜁𝑖𝑀𝑡 ]If we assume 𝜀𝑖𝑗𝑡 is a random variable drawn from independent extreme value distributions, thesecond term of right-hand side equation (16) becomes,
(17)

𝑃

𝑙𝑛𝑃

𝑖ℎ𝑡
𝑖ℎ𝑡
𝐸[𝜀𝑖𝑗𝑡 |𝜁𝑖1𝑡 , … , 𝜁𝑖𝑀𝑡 ] = ∑𝑀
+ 𝑙𝑛𝑃𝑖𝑗𝑡 ]ℎ≠𝑗 𝐴ℎ [ 1−𝑃𝑖ℎ𝑡, which are correction terms invented by Dubin and McFadden (1984) to be used to purge
selection bias in the second step.

[2nd step: Predicting selection-bias corrected and instrumented wage]
A selection-bias corrected and instrumented wage equation (18) considers the decision oflabor force participation in the multinomial case. Furthermore, to eliminate the possible
endogeneity problem, I utilize the 2006 and 2007 tax reform, following Blundell, et. al.(1998).
That is, I add tax-reform dummies to the wage equation (18).
𝑃 𝑙𝑛𝑃

𝑖ℎ
𝑖ℎ
(18) log(𝑤𝑖𝑗 ) = 𝛼𝑖𝑗 + 𝛼𝑔𝑗1 𝐼(𝑡 ≥ 2006) + ∑3ℎ≠𝑗 𝐴ℎ [ 1−𝑃+ 𝑙𝑛𝑃𝑗𝑖 ] + 𝛽 𝑗 𝑋𝑖𝑗 + 𝛾 𝑗 𝑍𝑖𝑗 + 𝑢𝑖𝑗𝑖ℎ, where 𝑤𝑖𝑗𝑡 is after-tax wage rate, 𝐼 represents indicator function describing the period after thetax reform which is interacted with group-dummies (cohort × education) that is suggested by
Blundell, et. al.(1998). The matrix of 𝑍𝑖𝑗𝑡 is other potential instrument variables, such as lagged
wage rate, parents’ educational attainment, house-owner and co-resident with parents dummies,
industry, firm-size, occupation and regional dummies.

13

Following Kumar (2013), this reduced form of selection-bias corrected instrumented wage
equation (18) is estimated for each year, allowing the coefficients to vary by year.

[3rd step: Structural Labor Force Participation Equation]
Here, I limit the scope to two labor force states, non-working and working, to estimate
extensive margins for each type of workers. Since persons determine their work decision bycomparing reservation wage with offered wage, the structural equation has to include predictedwage rate and has the same independent variables as the structural equation for the hours work
equation in the final step. For male workers, the equation is estimated by random effect panel
probit5(19)

P(work = 1) = Φ(βlog𝑊𝑖𝑡 + 𝛾 𝑋𝑖𝑗𝑡 )The elasticity of a wage change on the probability of employment (extensive margin) can be
described below.
(20)𝜂̂𝑒 = 𝛽̂ ϕ/Φ

, where ϕ and Φare normal density function and its CDF evaluated at means of independent
variables.
On the other hand, the female labor participation equation is estimated by fixed effect panel
logit since, unlike male workers, there is sufficient variation in a dependent variable within
individuals in a survey time. The specific equation is,(21)

P(work = 1) = Λ(βlog𝑊𝑖𝑡 + 𝛾 𝑋𝑖𝑗𝑡 )5

Fixed panel model can distinguish unobserved heterogeneity among individuals. But, since, in relatively short time
horizon of data, most of male workers have not changed their work status over time, thus fixed model for labor
participation decision cannot be used here. However, for the equation of work participation of female, I use fixed
effect model because female work status has been frequently changed over time within each sample.

14

, and Λ indicates the logistic distribution. Utilizing the property of logit distribution, we canderive extensive margin as,(22)𝜂̂𝑒 = 𝛽̂ (1 − Λ)

, where Λ is evaluated at means of regressors.

[4th step: Structural Working Hour Equation]
Finally, I estimate a structural working hour equation with fixed effect and selection
correction term6 to consider individual heterogeneity and mitigate selection bias for 𝛽1 withsamples of non-regular workers and regular workers.
𝑃

𝑙𝑛𝑃

𝑗𝑖𝑡
(23) ℎ𝑖𝑗𝑡 = 𝛼𝑖 + 𝛽1 log(𝑤𝑖𝑗𝑡 ) + 𝐴𝑖𝑗𝑡 + 𝐴𝑖𝑗𝑡−1 + ∑3ℎ≠𝑗 𝐴𝑗 [ 𝑗𝑖𝑡+ 𝑙𝑛𝑃𝑗𝑖𝑡 ] + 𝛽2 𝑋𝑖𝑗𝑡 + 𝜀𝑖𝑗𝑡
1−𝑃
𝑖𝑗𝑡

Using the key parameter𝛽1, we can get the unconditional elasticity of wage change on
working hours as (𝛽1/ mean of hours worked for each working status individuals).

4. Data
In Japan, there was no comprehensive household panel survey that reflects on the societywide demographic composition, not focusing only on a specific group. The Keio Household
Panel Survey (KHPS) is the first comprehensive survey to respond such a demand and has been
implemented annually since 2004. The questionnaire covers comprehensive subjects such as
household composition, income, expenditure, assets, and housing of targets households inaddition to school attendance, employment, and health condition of the respondents.

6

The estimation procedure is small extension of Semykina and Wooldridge (2010). They discuss that, in the panelsetting with censored model, we can get consistent estimator by fixed-effect 2SLS. That is, 1) for each time unit, use
probit to get the inverse mills ratio, then 2) use them in the second stage (fixed effect panel regression) to purge
selection bias. Here, I replace inverse mills ratio with the correction term by Dubin and McFadden (1984).

15

I focus on males between the ages of 18 and 65 years who are heads of households. Several
men are excluded from data in the analysis, such as 1) self-employed workers7, 2) who aresecondary earners in households, 3) who fails to report relevant variables during survey years(2004 – 2010).
Besides, I limit females who are between 18 and 65 years old, 1) single earner household,and 2) wives whose husbands are heads of households. As a result, I assume that only basic
allowance and employment income deductions are applicable for their income.
Lastly, I mention about construction of key variables below.
Working Hours
The questionnaire asks subjects about how long they work per weeks and also their overwork
hours. I employ working hours including overwork as a dependent variable.
Wages
The KHPS has different measure of wage rates, such as, daily, weekly, monthly and annuallywage data. Typically, hourly wage rate which is calculated by annual income divided by annual
working hours is used in literatures, but it is frequently pointed that this measure causes negative“division bias” in labor supply estimation. To purge the division bias, I follow the suggestion ofKimmel and Kniesner (1998). Since the problem is caused by the direct negative correlation
between working hours (a dependent variable) and hourly wage which is created by dividing
wage by work hours, the hourly wage rate which is the key regressor is made by dividing annual
total labor income by the products of reported months worked and working days in a month, anda norm for usual hours worked per week8.
Non-labor income and Assets
7

Reported income by self-employed might have lots of uncertainty. Thus, I do not use the data.
I set typical working hours as 40 and 35 hours per week for male regular and non-regular workers respectively. On
the other hand, I assign 40 and 25 hours per week for female regular and non-regular workers.
816

Non-labor income is calculated as the household income minus individuals’ labor income. In
the KHPS, several types of assets are reported. Here, I employ household net saving data which
is used in the 4th estimation. As Ziliak and Kniesner (1999) note, assets data also has potential
endogeneity problem. Based on their discussion, I instrument net saving with t-1 and t-2 laggedsaving, t-1 lagged wage rate and non-labor income, and t-1 lagged debt considering interest rate.
Marginal Tax Rate
I use the method of MaCurdy et al. (1990) to create smoothing marginal tax rate function. Its
specification is,(23)𝜏𝑡 = ∑𝑛,𝑖𝑗 (𝛷1𝑡𝑗 − 𝛷2𝑡𝑗 )𝑏𝑗 (𝐼𝑡 ) + (𝛷2𝑡𝑗 − 𝛷3𝑡𝑗 )𝜏̅𝑗

, where j refers national income tax system and inhabitant tax system, 𝑏𝑗 (𝐼𝑡 ) is an estimatedpolynomial in taxable income (𝐼𝑡 ), 𝜏̅𝑗 is a top income bracket for each tax system. 𝛷𝑖𝑡𝑗 is the
cumulative distribution function for the standard normal, and its mean is set as highest income
level over which top income tax is applied in each tax system and its standard deviation is set as
1.
The polynomial taxable income function is approximated by tax brackets within a given range
in each tax system. For example, before the 2007 tax reform, the cubic ordinary least squares
regression yields,(24) National Income Tax:

𝑏𝑛 (𝐼𝑡 ) = 0.065796 + (3.06−4 )𝐼𝑡 − (1.39−7 )𝐼𝑡2 + (2.95−11 )𝐼𝑡3(25) Inhabitant Income Tax: 𝑏𝑛 (𝐼𝑡 ) = 0.041717 + (1.82−4 )𝐼𝑡 − (1.20−7 )𝐼𝑡2 + (2.55−11 )𝐼𝑡3
The fixed rates of tax reduction are applied for each equation, considering the feature of thismeasure, that it, over some income thresholds, the amounts of tax reduction are fixed.

17

5. Estimated Wage and Employment Elasticities
Table3 shows results of labor supply elasticities from random effects employment probability
and fixed effect hours worked equation in the 3rd step and the 4th step for male workers aged 18
to 65 in Japan.
Wage elasticities of male workers are quite small and insignificant for both types of workers,
while comparison between non-regular and regular workers in extensive margin indicates show
significant difference. Extensive margins of total and regular workers are very small which
implies their labor force participation decision quite unresponsive against change of wages.
However, non-regular workers are quite sensitive to wage change, especially regarding labor
participation decision.
To compare to the female responses, I also calculate both margins of females. Table 4
suggests that female responses of both types of workers are much larger than those of males.
This is compatible with findings in Japan and other countries. The interesting feature is that,while the extensive margin of female regular workers is larger than non-regular workers, theintensive margin is as small as male workers. According to Hayashi (2009) who survey labor
supply literatures in Japan, for wage elasticity of all females, it is 0.122 on average and extensive
margin is 0.107. The results are relatively bigger than the average of Japanese findings, but it isfair to say the values are plausible in other countries’ studies.
Back to the results of male workers, we have to look at this result with a caution, because,even in non-regular work, we can assume there are different working patterns. As
aforementioned, Syokutaku is considered to have quite different working style compared to othernon-regular worker. Thus, to distinguish this effect, I will separate data by age groups (under andover 55 years old) to observed more detailed responses.

18

Table 3

Estimated Labor Supply Elasticities aged 18-65
Male Non-Regular
Whole Male Workers
Male Regular Workers
Wokers
0.0025***0.365***
0.0023***Extensive Margin(8.79)(9.32)(13.05)[0.0017][0.0392][0.0002]
0.009
0.022
0.046
Intensive Margin(1.18)(0.77)(0.56)[0.0070][0.0288][0.0083]a) z-value and t-value are reported in parentheses for extensive and intensive margins respectively.
Squared brackets show standard erros.
b) Observations are 4963, 669 and 4626 for extensive margin estimations of each type of workers.
For intensive margin estimations, observations are 4631, 337 and 4294.

Table 4

Estimated Labor Supply Elasticities for Women aged 18-65
Female Non-Regular
Whole Female Workers
Female Regular Workers
Wokers
0.587***
0.544***
0.746***
Extensive Margin(11.23)(9.37)(5.06)[0.0571][0.0609][0.1475]
0.262***
0.426***-0.035Intensive Margin(8.89)(9.09)(-0.83)[0.0294][0.0469][0.0042]a) z-value and t-value are reported in parentheses for extensive and intensive margins respectively.
Squared brackets show standard erros.
b) Observations are 1743, 1494 and 345 for extensive margin estimations of each type of workers.
For intensive margin estimations, observations are 2797, 1927 and 870.

19

Estimation results for male workers aged 18-55
Here, I limit my focus on male workers whose age is between 18 and 54 years old eliminating
the effect of Shokutaku.
Comparing the previous result, the biggest feature is that the extensive margin of non-regular
workers shows much smaller. This is understandable since the prime-age non-regular workers
could not get enough money, so that they would be urged to work more, while elder non-regular
workers could have received retirement payment, so that their motivation for work would be lessthan other non-regular workers.
Also, we can see the intensive margin for each worker is decreased (but, all are insignificant).

Table 5

Estimated Labor Supply Elasticities aged 18-54
Male Non-Regular
Whole Male Workers
Male Regular Workers
Wokers
0.0028***0.013***
0.0025***Extensive Margin(8.44)(4.24)(8.30)[0.0003][0.0031][0.0003]
0.003-0.0590.003
Intensive Margin(0.27)(1.54)(0.35)[0.0094][0.0389][0.0097]a) z-value and t-value are reported in parentheses for extensive and intensive margins respectively.
Squared brackets show standard erros.
b) Observations are 3449, 140 and 3347 for extensive margin estimations of each type of workers.
For intensive margin estimations, observations are 3411, 102 and 3309.

Estimation results for male workers aged 55-65
Table 6 shows elderly worker’s labor supply responses. As we can expect from previous
discussions, elderly non-regular workers are sensitive against wage changes in extensive margin.
For intensive margin, coefficients become significant for whole and non-regular workers, which20also ensure flexible working style of Shokutaku. However, still elderly regular worker indicatessimilar working patterns of that of prime-age males.

Table 6

Estimated Labor Supply Elasticities aged 55-65
Male Non-Regular
Whole Male Workers
Male Regular Workers
Wokers
0.023***
0.727***
0.035***
Extensive Margin
11.80
8.79
10.23
[0.0020][0.0827][0.0034]
0.0303*0.0871**0.001
Intensive Margin(1.10)(1.71)(0.40)[0.0162][0.0423][0.0169]a) z-value and t-value are reported in parentheses for extensive and intensive margins respectively.
Squared brackets show standard erros.
b) Observations are 1514, 529 and 1279 for extensive margin estimations of each type of workers.
For intensive margin estimations, observations are 1220, 235 and 985.

6. Conclusion
In this paper, using Japanese micro-panel data (KHPS) from 2004 to 2010, I bring new
evidences on Japanese male and female labor supply literatures. In my best knowledge, this is
the first comprehensive study for labor supply in Japan.
The obtained results show plausible range of extensive and intensive margins for both
workers. While intensive margins of male non-regular and regular workers aged 18 to 65 arequiet small and insignificant, those of females are much bigger as previous literatures suggest.
Also, we can see same thing in the extensive margin, that imply females labor decision is much
more responsive against wage changes.
The further investigation by separating data to two age groups for males reveals that elderly
male non-regular workers are more flexible on their work decision.

21

Summary Statistics
Table7

Summary Statistics for male workers (18-65 years old)
All menNon-regular worker

Regular worker

Average

S.D

Average

S.D

Average

S.D

Before-tax hourly wage

2919.79

2502.811756.32

1276.293237.532499.13After-tax hourly wage

2128.04

1694.67

1442.17907.042346.89

1679.17

Hours worked per week

44.51

19.40

39.29

15.50

48.13

16.04

Labor Force Participation

0.92

0.27

Non-labor income

73.93

401.06165.34436.3542.37

370.86

Saving628.941034.33600.23920.78578.71

942.19

Age

47.73

9.93

55.25

10.45

46.18

9.10

Tenure

18.28

11.68

16.77

15.21

19.01

10.77

Year of education

13.75

2.26

12.58

2.24

13.93

2.20

Number of Children (< 6 years)
Observations

0.28
4964

0.59

0.07
337

0.30

0.32
4297

0.62

Table8--

Summary Statistics for female workers (18-65 years old)
All menNon-regular worker
Average

S.D

Average

Regular worker

S.D

Average

S.D

Before-tax hourly wage

580.80961.98741.66552.001928.071468.21After-tax hourly wage

492.05738.35681.87445.891514.761037.75

Hours worked per week

15.95

19.04

24.83

14.12

41.19

14.65

Labor Force Participation

0.53

0.50

Non-labor income

487.96474.14469.30402.30363.76502.66

Saving709.251220.54469.31820.03632.57946.41

Age

46.93

10.28

46.87

8.67

46.01

9.07

Tenure

5.72

7.40

7.47

5.40

13.95

8.72

Year of education

12.85

1.71

12.73

1.59

13.06

1.76

Number of Children (< 6 years)
Observations

0.29
5369

0.60

0.14
1935

0.42

0.19
873

0.48

22

-

-

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24